8. i-SAS_ResponseKalmanFilter¶
8.1. interface¶
-
class
response_kalman_filter.interface.Interface(instance_name, input_names, output_names, structural_model_name=None, **kwargs)¶ Bases:
objectinterface class of analysis solver
-
package_name= 'response_kalman_filter'¶
-
structural_model_required= False¶
-
__init__(instance_name, input_names, output_names, structural_model_name=None, **kwargs)¶ initialization of interface class of analysis solver
- Parameters
instance_name (str) – instance name.
input_names (dict) – dict whose keys are names of input quantity and values are names of input data.
output_names (dict) – dict whose keys are names of output quantity and values are names of output data.
structural_model_name (str, optional) – structural model name.
**kwargs – Arbitrary keyword arguments.
Example
>>> input_names = {'strain': ['rosette_strain_x', 'rosette_strain_y', 'rosette_strain_xy']} >>> output_names = {'displacement': ['analysis_displacement_z'], 'stress':['analysis_stress_x']} >>> interface = Interface(input_names, output_names, 'beam')
-
set_project(project_name)¶ - Parameters
project_name (str) – project name.
-
set_model(sensing_output_metadata, analysis_output_metadata, structural_models=None, sensor_structural_model_connections=None, analysis_structural_model_connections=None, streaming=False)¶ set structural model
- Parameters
sensing_output_metadata (pd.DataFrame) – metadata of sensing output.
analysis_output_metadata (pd.DataFrame) – metadata of analysis output.
structural_models (dict, optional) – structural model.
sensor_structural_model_connections (dict, optional) – connections between sensor and structural model.
analysis_structural_model_connection (dict, optional) – connections between analysis and structural model.
streaming (bool, optional) – if true, run as structural_analysis.
- Returns
static data to be exported.
- Return type
dict
-
__call__(input_data)¶ calculate quantity of state
- Parameters
input_data (dict) – data calculated before
- Returns
- containing:
dict: analysis results. pandas.DataFrame: timestamp used. The length is the same as analysis results.
- Return type
tuple
-
exit()¶ exit solver
-
8.2. model¶
-
class
response_kalman_filter.model.Model(input_names, output_names, structural_model_name, cfg)¶ Bases:
objectmodel class
-
coord_sys= {'new_strain': 'local'}¶
-
__init__(input_names, output_names, structural_model_name, cfg)¶ constructor
- Parameters
input_names (dict) – dict whose keys are names of input quantity and values are names of input data.
output_names (dict) – dict whose keys are names of output quantity and values are names of output data.
structural_model_name (str, optional) – structural model name.
cfg (dict) – config.
-
set_model(sensing_output_metadata, analysis_output_metadata, structural_models, sensor_structural_model_connections, analysis_structural_model_connections, streaming)¶ set model and calculate intermediate values
- Parameters
sensing_output_metadata (pd.DataFrame) – metadata of sensing output.
analysis_output_metadata (pd.DataFrame) – metadata of analysis output.
structural_models (dict) – structural model.
sensor_structural_model_connections (dict) – connections between sensor and structural model.
analysis_structural_model_connection (dict) – connections between analysis and structural model.
streaming (bool) – if true, run as structural_analysis.
-
function(input_data)¶ example function
- Parameters
input_data (dict) – input data.
- Returns
output data.
- Return type
dict
-
__call__(data)¶ calculate results from input using functions
- Parameters
data (dict) – input data.
- Returns
- containing followings,
- dict: result data. the key is output_quantity_name, and the value is the list, which
has data value (the type is pandas.DataFrame) as element corresponding to its data name.
- dict: used timestamp for each input data. Dict has two keys, ‘s’ as strating timestamp and
’e’ as end time stamp, and the value is 1d-array of the timestamp used to caclulate output, whose length is the same as the length of output.
- Return type
tuple
-